数据显示，截至2025年11月28日，中证人工智能主题指数(930713)前十大权重股分别为中际旭创(300308)、新易盛(300502)、寒武纪(688256)、中科曙光 (603019)、澜起科技(688008)、科大讯飞(002230)、海康威视(002415)、豪威集团(603501)、金山办公(688111)、浪潮信息(000977)，前十大权重股合计占比 63.92%。（以上所列股票仅为指数成份股，无特定推荐之意） 近期，智普AI宣布开源Open-AutoGLM框架和AutoGLM-Phone-9B核心模型，标志着大模型竞赛已经跨过"能说会道"的LLM（大型语言模型）思辨阶段，正 式迈入"能做能行"的Agent（智能体）行动时代。 截至2025年12月10日 14:42，中证人工智能主题指数(930713)上涨0.34%，成分股中科星图(688568)上涨6.49%，光迅科技(002281)上涨3.46%，新易盛(300502) 上涨3.40%，豪威集团(603501)上涨2.85%，科沃斯(603486)上涨1.88%。AI人工智能ETF(512930)上涨0.28%， 冲击5连涨。最新价报2 ...

央广网北京12月10日消息（总台中国之声记者韩萌）据中央广播电视总台中国之声报道，近段时 间，我国AI应用市场按下"加速键"，多款AI助手集体"上新"。它们不再只是能聊天的"百科全书"，而是 争相成为能替你订票、办事，甚至模拟一些智能场景的生活伙伴。一场关于"谁的AI更实用"的竞赛，已 经在我们每个人的手机桌面上悄然打响。 那么这波密集的"上新"潮，究竟是AI深度融入我们的生活，还是可能陷入同质化的竞争？什么样的 AI应用，才能最终留在用户的手机里呢？ 11月17日上线的阿里巴巴"千问"App，公测一周的下载量就突破1000万次，超越了ChatGPT、Sora 等国际应用初期的增长速度。阿里巴巴对其的定位是"会聊天能办事的个人AI助手"，其目标在于将AI能 力与庞大的电商、本地生活生态深度整合，让AI不仅能回答问题，更能真正完成跨场景的服务。 仅仅一天后，蚂蚁集团的AI助手"灵光"也对外发布。"灵光"选择了一条更具颠覆性的路径，开创性 地让用户在移动端用自然语言对话，最快30秒就能生成一个可交互、可分享的"闪应用"。这种将应用开 发门槛降至近乎为零的能力，迅速激发了普通用户的创造力。灵光用户运营高级专家程沉介 ...

【#腾讯大模型改名##腾讯混元海外版名称简化#】腾讯宣布将其大模型品牌"混元（Tencent Hunyuan）"更名为更简化的 "Tencent HY"。 这是一次"名字更短、传播更快"的品牌优化。HY的两字母结构与当前国际大模型的主流命名方式趋 同，具备短、轻、易扩展的特性。 像OpenAI的GPT-4o、谷歌的Gemini 3 Pro等命名都指向一个共同趋势：在大模型迭代极快的周期下，突 出数字化的代际演进，往往比强调语义本身更能让用户直接感知模型的能力升级。 清晰的代际编号不仅便于产品矩阵管理，也方便开发者追踪技术版本、判断性能变化和选择适配的部署 方案。 在这样的行业语境中，HY作为基础名称，未来可以通过类似"HY-1、HY-2"或"HY-Pro、HY-Max"这样 的结构自然展开，使技术迭代的节奏更容易被用户捕捉，而腾讯也正是这样做的。 名称越轻、越结构化，就越适合作为长期的模型体系入口，而不是被一次性产品叠加所稀释。 过去一年中，混元既是模型、也是产品品牌，同时承担API端、企业端、消费端多重角色，名称本身带 有一定文化气质，删繁就简使用简写的"HY"，保留文化含义之余，中国人看得懂，让外国人记得 ...

Group 1 - The Hong Kong stock market continues to experience fluctuations, with mixed performances among tech giants, as Meituan-W rises nearly 2%, while Alibaba-W, Xiaomi Group-W follow suit, and Tencent Holdings, Kuaishou-W, and Bilibili-W show declines [1][6] - Alibaba has established the Qianwen C-end business group, merging its previous smart information and smart interconnection units, aiming to create a super app as the primary entry point for users in the AI era [3][10] - Citic Securities remains optimistic about the internet sector's cyclical attributes combined with the upward trend of AI, suggesting that if AI technology exceeds expectations, it is likely to be driven by internet giants and their partners [10][11] Group 2 - The Hong Kong Internet ETF (513770) has seen significant capital inflow, with a total of 175 million yuan over four consecutive days, indicating strong buying interest in AI-related stocks [11][13] - The ETF tracks the CSI Hong Kong Internet Index, with its top ten holdings focusing on AI cloud computing and applications, accounting for over 73% of the portfolio, showcasing a strong leader advantage [13] - The ETF's latest scale exceeds 10 billion yuan, with an average daily trading volume of over 600 million yuan, providing good liquidity and supporting T+0 trading without QDII quota restrictions [13]

证券时报·券商中国记者日前获悉，阿里已成立千问C端事业群，由阿里巴巴集团副总裁吴嘉负责。 据悉，该事业群由原智能信息与智能互联两个事业群合并重组而来，包含千问APP、夸克、AI硬件、UC、书 旗等业务。 这也是今年9月宣布的额外AI基础设施投入的一部分。今年9月，吴泳铭概述了他自己推出新模型和"全栈"AI 技术的计划，这反映了阿里巴巴既要开发服务，也要开发支撑该技术的基础设施的意图。 9月24日，阿里巴巴集团CEO、阿里云智能集团董事长兼CEO吴泳铭在云栖大会演讲中表示，大模型是下一代 操作系统，而AI云是下一代计算机。也许未来全世界只会有五六个超级云计算平台。目前阿里正积极推进 3800亿元的AI基础设施建设，并计划追加更大的投入。 吴泳铭认为，实现AGI（通用人工智能）已是确定性事件，但这仅是起点，终极目标是发展出能自我迭代、 全面超越人类的ASI（超级人工智能），以解决气候、能源、星际旅行等重大科学难题。 通往超级人工智能之路分为三个阶段：一是"智能涌现"，AI通过学习人类知识具备泛化智能；二是"自主行 动"，AI掌握工具使用和编程能力以"辅助人"，这是行业当前所处的阶段；三是"自我迭代"，AI通过连接 ...

Core Insights - The consumer finance industry is experiencing a "stable quantity and improved quality" development trend, supported by policy initiatives and digital transformation, with consumer loans reaching 21.29 trillion yuan by Q3 2025 [1][6] - Digital transformation is a core driving force for the industry, with over 90% of institutions engaging in key digital projects, and 68% focusing on enhancing user activity and transaction volume [1][6] Group 1: Industry Development Overview - The balance of consumer loans reached 21.29 trillion yuan by Q3 2025, with 715 million active credit cards and 1.35 trillion yuan in loans from licensed consumer finance companies, indicating a shift from scale expansion to high-quality development [1][6] - Consumer spending policies are being implemented, with service retail growth outpacing goods retail by 0.2 percentage points, and per capita disposable income and consumption steadily increasing [1][6] - The industry is facing challenges such as intensified competition and difficulties in customer acquisition, leading to performance differentiation among licensed consumer finance companies [2][6] Group 2: Digital Transformation - Over 90% of institutions are implementing key digital projects, with significant applications of AI and large models in marketing, risk control, and customer service [1][6] - 68% of institutions prioritize increasing user activity and transaction volume as a key operational focus, with technology investments exceeding 3% of operating revenue for most institutions [1][6] - Half of the institutions can complete credit card approvals within 10 minutes, and over 40% report improved customer acquisition through self-operated channels, with lower costs for offline acquisition compared to online [1][6] Group 3: User Demand and Experience - The 22-35 age group constitutes over 50% of the consumer finance market, with credit card users averaging 10.4 transactions per month, spending 5,329 yuan [2][6] - Users prefer practical benefits, with promotional activities and interest-free periods being the main motivations for applying for credit cards [2][6] - 38% of users report that card applications take 10-30 minutes, with app downloads and facial recognition being the most time-consuming processes [2][6] Group 4: Future Directions - The industry needs to focus on optimizing customer experience by simplifying application processes and benefit rules, and strengthening customer service systems [2][6] - There is a call for deeper digital construction and the application of AI and large model technologies [2][6] - The integration of credit cards with retail products and the development of differentiated management systems for segmented customer groups are recommended to achieve sustainable high-quality business growth [2][6]

Group 1 - Huawei's 2012 Lab has established a foundational model department focused on advancing base model development [1] - The name "2012 Lab" is inspired by Huawei's founder Ren Zhengfei's belief that the future digital world will face an information explosion, akin to a flood depicted in the movie "2012" [1] - The lab comprises dozens of research units dedicated to cutting-edge technologies such as communication, cloud computing, audio-video analysis, artificial intelligence, and machine learning, targeting research directions for the next 5-10 years [1] Group 2 - In October, Huawei's Executive Director Yu Chengdong announced a global recruitment initiative for top AI talents with exceptional academic backgrounds and innovative spirits [1] - The recruitment emphasizes three core selection criteria: academic excellence, passion for AI, and innovative thinking [1] - Positions cover advanced areas such as AI algorithms, large model architecture, multimodal understanding, data engineering, and AI security and privacy, with some roles directly involved in Huawei's "Pangu Model" next-generation evolution and AGI exploration projects [5]

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving sector, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new advanced course focused on end-to-end production in autonomous driving has been designed, emphasizing practical applications and real-world experience [2][4] Course Overview - The course is structured to cover various core algorithms, including one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - The course aims to provide in-depth knowledge and practical skills necessary for production in autonomous driving, with a focus on real-world applications and challenges [2][4] Chapter Summaries - **Chapter 1: Overview of End-to-End Tasks** Discusses the integration of perception tasks and the learning-based design of control algorithms, which are essential skills for companies in the end-to-end era [7] - **Chapter 2: Two-Stage End-to-End Algorithm Framework** Introduces the modeling methods of two-stage frameworks and the information transfer between perception and planning, including practical examples [8] - **Chapter 3: One-Stage End-to-End Algorithm** Focuses on one-stage frameworks that allow for lossless information transfer, presenting various methods and practical learning experiences [9] - **Chapter 4: Production Application of Navigation Information** Covers the critical role of navigation information in autonomous driving, detailing mainstream navigation map formats and their integration into models [10] - **Chapter 5: Introduction to RL Algorithms in Autonomous Driving** Explains the necessity of reinforcement learning in conjunction with imitation learning to enhance the model's ability to generalize [11] - **Chapter 6: Trajectory Output Optimization** Engages participants in practical projects focusing on algorithms based on imitation learning and reinforcement learning [12] - **Chapter 7: Safety Net Solutions - Spatiotemporal Joint Planning** Discusses post-processing logic to ensure model accuracy and stability in trajectory outputs, introducing common smoothing algorithms [13] - **Chapter 8: Experience Sharing on End-to-End Production** Provides insights on practical experiences in production, addressing data, models, scenarios, and strategies for system capability enhancement [14] Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][17]

Core Insights - Alibaba has intensified its focus on the AI sector, particularly in the consumer (To C) market, by establishing the "Qianwen C-end Business Group" to enhance its AI offerings [1][2] Group 1: Organizational Changes - The "Qianwen C-end Business Group" was formed by merging the original Intelligent Information Business Group and the Intelligent Connectivity Business Group, with a clear goal to develop "Qianwen" into a super app for AI services [1] - Wu Jia, a vice president at Alibaba, has been appointed to lead the new group, indicating a strategic shift towards consumer-focused AI applications [2] - The restructuring is seen as a culmination of previous organizational adjustments aimed at consolidating AI efforts within the company [2] Group 2: Strategic Goals - The core objective of the Qianwen C-end Business Group is to create a leading AI application that serves as the primary entry point for various AI services in the consumer market [1][6] - Alibaba's long-term vision includes expanding the Qianwen app into a cross-device AI assistant, integrating with smart glasses, personal computers, and automobiles [1] Group 3: AI Product Development - In March, Alibaba launched the "New Quark," an AI flagship application that integrates multiple intelligent functions, moving away from traditional search models [4] - The Quark app aims to provide a comprehensive AI experience, with plans for continuous updates based on advancements in AI models [4] - The company has also introduced six models of AI glasses, enhancing its hardware offerings and integrating them with various Alibaba ecosystem services [4] Group 4: Market Positioning - The rebranding of the dialogue application "Tongyi" to "Qianwen" reflects a strategic pivot towards positioning the app as a key player in the AI consumer market [5][6] - The Qianwen app is designed to be a personal AI assistant capable of both conversation and task execution, aligning with the company's vision of becoming a leader in AI-driven consumer applications [6]

Core Insights - The company "月之暗面" is regaining public attention with recent developments, including the launch of subscription services and preparations for an IPO, as highlighted by its president Zhang Yutong [1][5][11] - The company emphasizes its strategic focus on core technological innovations and productivity tasks, distancing itself from entertainment and homogeneous competition [1][8] Company Developments - Zhang Yutong presented the latest advancements in the Kimi model's performance and product offerings at a Tsinghua University event, marking a significant return to the spotlight after a year of scrutiny [1][5] - The company has launched a subscription model for Kimi For Coding and introduced the Kimi K2Thinking model, which supports real-time tool usage [1][10] - There are indications that the company is preparing for an IPO, with analysts suggesting that the current market conditions may favor such a move [5][11] Market Position and Strategy - 月之暗面 is noted for its low valuation compared to leading U.S. model companies, operating with less than 1% of their resources while still achieving significant technological advancements [2] - The company aims to overcome data limitations rather than computational power, achieving efficiency improvements with the Kimi K2 model [4] - The focus is on niche areas such as complex task management and productivity, rather than competing directly with larger players in the entertainment sector [8][9] User Engagement and Performance - Kimi has approximately 9.67 million monthly active users, ranking fifth among native AI applications, while competitors like Doubao and DeepSeek have significantly higher user bases [7] - The company has shifted its strategy away from user scale competition, focusing instead on its unique strengths in technology and product offerings [8] Commercialization and Partnerships - 月之暗面 is pursuing a direct commercialization strategy for its consumer offerings, particularly in computationally intensive tasks, while maintaining free access for basic interactions [9][10] - The company has secured partnerships with notable platforms, integrating its Kimi K2 model into various applications, indicating a strong position in the B2B market [10]